We develop CACE (Constraints And Correlations mining Engine), a framework that significantly improves the recognition accuracy of complex daily activities in multi-inhabitant smarthomes. CACE views the implicit relationships between the activities of multiple people as an asset, and exploits such constraints and correlations in a hierarchical fashion, taking advantage of both personspecific sensor data (generated by wearable devices) and person-independent ambient sensor data (generated by ambient sensors). To effectively utilize such couplings, CACE first uses a multi-target particle filtering approach over ambient sensors captured movement data, to identify the number of distinct users and infer individual-specific movement trajectories. We then utilize a Hierarchical Dynamic Bayesian Network (HDBN)-based model for activity recognition. This model utilizes the inter-and-intra individual correlations and constraints, at both micro-activity and macro-activity levels, to recognize individual activities accurately. These constraints are learnt automatically using data-mining techniques, and help to dramatically reduce the computational complexity of HDBN-based inferencing. Empirical studies using a real-world testbed of five multi-inhabitant smarthomes shows that CACE is able to achieve an activity recognition accuracy of ≈ 95%, with a 16-fold reduction in computational overhead compared to traditional hybrid classification approaches.